Personalized information query suggestions

ABSTRACT

Personalized search or query suggestions associated with one or more persons and/or content items are provided. A suggestion application learns from user behavior within the suggestion application and presents suggestions for allowing the user to search or navigate to one or more people of particular interest or relevance to the user and for allowing the user to search or navigate to one or more content items associated with people and activities of particular interest or relevance to the user. Two types of suggestions are provided to the user. A first type of suggestion involves suggesting one or more people that may be of particular relevance or interest to the querying user. A second type of suggestion includes a textual suggestion comprised of a person (actor) and an associated action.

BACKGROUND

Information users and workers gather and process enormous amounts ofinformation for business, education, and pleasure. Typical informationusers or workers utilize hundreds (or more) of documents, images,electronic communications, data sets and the like. In addition, atypical information user or worker gathers and/or consumes equally largeamounts of information through a variety of search mechanisms, forexample, file or data search applications, Internet browsingapplications, and the like.

In a typical enterprise setting, an information user/worker may workwith a number of different persons on a number of projects, each beingassociated with many documents and other content items. The informationuser/worker may have different and sometimes complex relationships withthe various people, for example, manager-to-report, peer-to-peer, etc.Other types of relationships between people (users) are manifested insocial and collaborative connections, for example, electroniccommunications between people, collaboration on content items (e.g.,documents) between people, social commentary and communication betweenpeople, and the like. Other types of relationship information for agiven user may include relationship information between the user anddocuments or other content items. For example, various content items maybe created, edited and/or viewed by the user, or various content itemsmay be created, edited and/or viewed by other people with whom the userhas a relationship, including a person-to-person relationship or aperson-to-content item relationship.

According to a typical search mechanism, a user may be able to search ona person or content item, and a flat list of information may be returnedthat may include a person or item of interest, but may not surface aperson or content item to the user of particular interest or relevanceto the user. That is, the user may have to parse a list of returnedsearch results for one or more people or content items to find a personor content item that is responsive to the search.

There is a need for query suggestion that provides searching suggestionsto a user for searching or navigating to one or more people ofparticular importance or relevance to the user and for providingsuggestions for allowing a user to search or navigate to content itemsassociated with actions or activities of a given person, including theuser.

It is with respect to these and other considerations that the presentinvention has been made.

SUMMARY

This summary is provided to introduce a selection of concepts in asimplified form that are further described below in the detaileddescription. This summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended asan aid in determining the scope of the claimed subject matter.

Embodiments of the present invention solve the above and other problemsby providing personalized search or query suggestions associated withone or more persons and/or content items. According to embodiments, asuggestion application, is provided that learns from user behaviorwithin the suggestion application and that presents suggestions forallowing the user to search or navigate to one or more people ofparticular interest or relevance to the user and for allowing the userto search or navigate to one or more content items associated withpeople and activities of particular interest or relevance to the user.In a first instance, people and content oriented suggestions aregenerated based on past user behavior with respect to the suggestionapplication. However, if user behavior history is not established withrespect to the suggestion application (e.g., on first use of thesuggestion application by the user), then user behavior with respect toother applications and associated people and content items interactedwith by the user may be used to provide suggestions to the user that aremost relevant and interesting to the user.

In response to a user query, two types of suggestions may be provided tothe user. A first type of suggestion involves suggesting one or morepeople that may be of particular relevance or interest to the queryinguser. A second type of suggestion includes a textual suggestioncomprised of a person (actor) and an associated action. For example, aquery input into a suggestion application search field of the textcharacter “P” may return a first suggestion of people named “Paul” or“Pamela” and a second textual suggestion of “edited by Paul” or “emailedto me from Pamela.” Suggestions provided to a querying user aredetermined by selecting top suggestions ranked against other suggestionsbased on a scoring model applied to the people and actions comprisingthe suggestions.

The details of one or more embodiments are set forth in the accompanyingdrawings and description below. Other features and advantages will beapparent from a reading of the following detailed description and areview of the associated drawings. It is to be understood that thefollowing detailed description is explanatory only and is notrestrictive of the invention as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this disclosure, illustrate various embodiments of the presentinvention.

FIG. 1 is a block diagram of one embodiment of a system for providingcollected and aggregated information from which personalized querysuggestions may be generated.

FIG. 2 is a flow chart illustrating a method for providing personalizedquery suggestions.

FIG. 3A is an illustration of an example information page comprising agrid of aggregated content items from which a query may be initiated forreceiving one or more personalized query suggestions.

FIG. 3B is an illustration of an example information page comprising agrid of aggregated content items from which a query may be initiated forreceiving one or more personalized query suggestions and showing a queryor search field for receiving a search or query input from a user.

FIG. 3C is an illustration of an example information page showing anumber of personalized query suggestions provided to a user in responseto a search query.

FIG. 3D is an illustration of an example information page showing anumber of personalized query suggestions provided to a user in responseto a search query and showing a user selection of a provided peoplequery suggestion.

FIG. 3E illustrates on or more information items associated with aparticular navigated to person based on a query suggestion for thenavigated-to person.

FIG. 3F is an illustration of an example information page showing anumber of personalized query suggestions provided to a user in responseto a search query and showing a user selection of a textual actor-actionquery suggestion.

FIG. 3G illustrates an example document or content item navigated to inresponse to a user selection of a suggested textual actor-action querysuggestion.

FIG. 4 is a block diagram illustrating example physical components of acomputing device with which embodiments of the invention may bepracticed;

FIGS. 5A and 5B are simplified block diagrams of a mobile computingdevice with which embodiments of the present invention may be practiced;and

FIG. 6 is a simplified block diagram of a distributed computing systemin which embodiments of the present invention may be practiced.

DETAILED DESCRIPTION

The following detailed description refers to the accompanying drawings.Wherever possible, the same reference numbers are used in the drawingand the following description to refer to the same or similar elements.While embodiments of the invention may be described, modifications,adaptations, and other implementations are possible. For example,substitutions, additions, or modifications may be made to the elementsillustrated in the drawings, and the methods described herein may bemodified by substituting, reordering, or adding stages to the disclosedmethods. Accordingly, the following detailed description does not limitthe invention, but instead, the proper scope of the invention is definedby the appended claims.

As briefly described above, embodiments of the present invention aredirected to providing personalized search or query suggestionsassociated with one or more persons and content items. In order togather both people (actors) and action information for developing thetwo types of query suggestions, briefly summarized above, a suggestionapplication 120 must obtain information on the querying user'sinteractions with other people and information on actions or activitiesassociated with the querying user, the other people, and associatedcontent items. As should be appreciated, each time the querying userinteracts with a given person, that interaction may be noted and storedfor subsequently determining a frequency of interactions between thequerying user and other people and a strength of relationship betweenthe querying user and other people, as may be determined based on suchfactors as how often the querying user interacts with other people,structural relationships between the querying user and other people, forexample, manager-to-report, peer-to-peer, and the like, or any of avariety of other types of information, such as organization charts thatassociate the querying user with one or more people, and the like.Similarly, information may be gathered about content items and actionsassociated with content items (e.g., editing, viewing, storing,presenting, mailing, and the like), for allowing the generation ofactor-action textual suggestions, as described above.

According to one embodiment, information gathered about the queryinguser, other people, content items, and actions or other activitiesassociated with the querying user, other people, and content items maybe gathered and aggregated and may be represented in a graph thatgraphically represents relationships between people, content items andactions/activities that may be interrogated by the suggestionapplication for generating query suggestions, as described herein. FIG.1 is a block diagram illustrating a system architecture 100 forgathering and aggregating information about people, content items andactivities which may be used by the suggestion application for providingpersonalized query suggestions, as described herein.

The system architecture 100 includes an aggregator 108 operable tocollect organizational relationship data 105 for various individuals(people) and activity data 106 associated with individuals 102A-B(collectively 102) and content items 103 from a plurality of informationsources 104A-N (collectively 104) and store the relationship data 105and activity data 106 in a graph 114. The information sources 104 mayinclude various types of workloads or information sources such as socialnetworking services, enterprise social network services, onlineproductivity software suites (which may include applications such as,but not limited to, a word processing application, a spreadsheetapplication, a slide presentation application, a notes takingapplication, a calendaring application, a video conferencing, an instantmessaging application, etc.), collaboration services, communicationsoftware, etc.

Activity data 106 may comprise various types of information such as, butnot limited to, presence data, interaction data, data associated withcommunications between people (e.g., emailing, messaging, conferencing,etc.), data associated with an individual's activity stream (e.g.,authoring or modifying a document, liking, commenting, following, orsharing a document, following a person, commenting on a feed, etc.),trending data, group membership (e.g., inclusion in a distribution list,attendee in a meeting invitation, etc.). Organizational relationshipdata 105 may comprise various types of information such as, but notlimited to, data associated with a project structure or organizationalstructure (e.g., who an individual works with, works for, is a peer to,directs, manages, is managed by, etc.).

As mentioned above, the organizational relationship data 105 andactivity data 106 may be stored in a graph 114. Activities and peoplerelationships may be stored as edges 112A-B (collectively 112), andindividuals 102 who act upon a content item 103 or interact with anotherindividual 102, content items 103 that are acted upon may be stored asnodes 110A-C (collectively 110). For example, a node 110 may include anindividual 102 (nodes 110A and 110C), a group of individuals, a contentitem 103 such as a document (node 110B), an email or other communicationtype, a webpage, etc.

An edge 112 may include various types of actions (i.e., activity edge112B) (e.g., like, comment, follow, share, authoring, modifying,communication, participation, etc.) and relationships (i.e.,relationship edge 112A). Consider for example that an individual 102“likes” a certain document (i.e., selects a “like” option associatedwith the document). The individual and the document (content item 103)may be stored as nodes 110 and the “like” selection may be stored as anedge 112.

A relationship edge 112A may include explicit relationships and/orimplicit relationships. Explicit relationships may include relationshipsdefined according to an organization structure and data (i.e.,organizational relationship data 105). For example, an explicitrelationship may include an individual's manager, peers, directs, etc.An explicit relationship may be stored as a relationship edge 112A suchas a manager edge, peer edge, directs edge, etc. Implicit relationshipsmay include relationships determined according to activity in one ormore workloads (i.e., activity data 106 from one or more informationsources 104). For example, an implicit relationship may include anindividual 102 following another individual on an enterprise socialnetwork service (information source 104), being included on adistribution list with another individual, is a co-author of a documentwith another individual, emailing (or other type of communication) withanother individual, group memberships, commenting on anotherindividual's feed, etc.

Edges 112 may also include inferred edges that may be created between afirst individual 102 and a content item 103 acted upon or a personinteracted with by a second individual 102 with whom the firstindividual 102 shares a relationship edge 112A. An inferred edge mayalso be created between a first individual 102 and a second individual102 when the second individual acts upon a content item 103 with whichthe first individual 102 shares an activity edge 112B. For example, afirst individual 102 named Ann may share a relationship edge 112A with asecond individual 102 named Bob. An inferred edge 112 may be createdbetween Ann and a content item 103 that Bob modifies.

The system 100 may comprise an analytics engine 115 operable tocalculate and apply weights on edges 112 according to what activity isperformed (e.g., a like, comment, share, follow, email, etc.) and therelationship between a first individual 102 and an individual(s) 102performing the activity. Weights may also be based on how recently anactivity was performed. A weight on a relationship edge 112A may bebased on implicit or explicit signals generated through activity on theplurality of workloads, such as an amount and type of activity anindividual 102 has with another person, a number of times an individual102 interacts with a content item 103, the type of interaction, etc. Forexample, if an individual 102 communicates via email with a firstinformation worker (IW) daily, and is frequently an attendee of meetingsthat the first IW is also an attendee of, the weight of a relationshipedge 112A between the individual 102 and the first IW may be higher thanthe weight of a relationship edge 112A between the individual 102 and asecond IW whom the individual 102 emails less frequently and who share acommon “like” of a document on a social network site. A weight on anactivity edge 112B may also be based on a type of activity. For example,an “edit” or “share” operation may be considered to be more importantthan a “like” operation, and thus may have a higher weighting than the“like” operation. An individual's relationship edges 112A and activityedges 112B may be ranked according to their calculated weights.

As described in detail below weights applied to people, content itemsand activities (or edges) associated with people, content items andactivities may be used for scoring people and actor-action suggestions.People and actor-action suggestions may then be ranked, and top X rankedsuggestions may be provided to a querying user 122 operating at his/hercomputing device 118.

The suggestion application 120 illustrated in association with computingdevice 118 is illustrative of a software application having sufficientcomputer executable instructions for enabling embodiments of the presentinvention as described herein. The application 120 may include a thickclient application, which may be stored locally on the computing device118, or may include a thin client application (i.e., web application)that may reside on a remote server and accessible over a network, suchas the Internet or an intranet. A thin client application may be hostedin a browser-controlled environment or coded in a browser-supportedlanguage and reliant on a common web browser to render the applicationexecutable on a computing device 118. The backend processing system 125is illustrative of one or more local or remote computing systems atwhich the system 100 or components thereof and at which functionalitiesof the suggestion application 120 may be maintained and operated.Alternatively, all components of the system 100, application 120 andbackend processing system 125 may operate as an integrated client-sideapplication for providing the embodiments of the invention describedherein.

According to embodiments, the suggestion application 120 learns fromuser behavior within the suggestion application and presents suggestionsfor allowing the user to search or navigate to one or more people ofparticular interest or relevance to the user and for allowing the userto search or navigate to one or more content items associated withpeople and activities of particular interest or relevance to the user.According to embodiments, people and content oriented suggestions arefirst generated based on past user behavior with respect to thesuggestion application. However, if user behavior history is notestablished with respect to the suggestion application (e.g., at firstuser of the suggestion application by the user), then user behavior withrespect to other applications and associated people and content itemsinteracted with by the user may be used to provide suggestions to theuser that are most relevant and interesting to the user. In addition,user behavior with respect to other applications may also be used in thecase where user behavior within the suggestion application isinsufficient or there is a need for a tie breaker between suggestions.

In response to a user query, two types of suggestions are provided tothe user. A first type of suggestion involves suggesting one or morepeople that may be of particular relevance or interest to the queryinguser. A second type of suggestion includes a textual suggestioncomprised of a person (actor) and associated action. For example, aquery input into a suggestion application search field of a textcharacter “P” may return a first suggestion of people named “Paul” or“Pamela” and a second textual suggestion of “edited by Paul” or “emailedto me from Pamela.” Suggestions provided to a querying user aredetermined by selecting top suggestions ranked against other suggestionsbased on a scoring model applied to the people and actions comprisingthe suggestions.

According to embodiments, people provided as suggestions to the user maybe identified because the user previously has followed suggestionsidentifying those people, or because the user has relationships withthose people according to a variety of relationship types (e.g., contentcollaboration, electronic communications, social network interactions,etc.). Actor-action textual suggestions may be constructed based onactions associated with different people, for example, an action ofediting a document associated with an identified person (i.e., theactor).

After the two types of suggestions are generated, graphicalrepresentations of the generated suggestions are displayed in asuggestions user interface for showing the suggestions to the user andfor allowing the user to select a suggestion for navigating tounderlying or associated information. A user selection of a peoplesuggestion may cause immediate navigation to information about theassociated person (e.g., “Paul”). A user selection of one of the textualactor-action suggestions may cause immediate navigation to a contentitem that is the subject of a textual actor-action suggestion. Forexample, a selection of a textual suggestion “emails to me from Pamela”may cause navigation to an electronic mail message from Pamela or to adocument that was transmitted to the user from Pamela.

FIG. 2 is a flow chart illustrating a method 200 for providingpersonalized query suggestions. According to embodiments, the two typesof query suggestions provided to the user are personalized for the usersuch that the suggestions for one user may be completely different fromsuggestions provided to a different user. In order to generate the twotypes of suggestions, the suggestion application and associated back-endprocessing systems gather, score and rank information for both types ofsuggestions so that a top X number of people (actor) suggestions may beprovided to the user in response to a query, and so that a top X numberof textual actor-action suggestions may be provided to the user.

The method 200 illustrated in FIG. 2 may be performed for generatingpersonalized query suggestions either before a user begins a query ordynamically in response to a query. At start operation 205, thesuggestion application 120 begins generation of personalized querysuggestions in either case. As described above, at an informationgathering operation 210, the suggestion application 120 gathersinformation on people, content items and actions or activitiesassociated therewith for generating suggestions for a given user. Asdescribed above with reference to FIG. 1, information may be gathered,aggregated, graphed and weighted or assisting in a scoring operationthat is applied to one or more people that may be the subjects of peoplequery suggestions provided to the querying user.

At scoring operation 215, for the first type of suggestion (i.e., peoplesuggestion), a first scoring factor is computed based on how often theuser for which the suggestions are being generated has used suggestionswithin the suggestion application directed to people from a set of lastX people uses. That is, for the last X (e.g., 10) people suggestionsselected for acting on by the user, a determination is made as to howoften the user has selected on or acted on a suggestion for a particularperson.

A next scoring factor is computed based on how much interaction the userhas had with a person from interactions with other applications (e.g.,electronic mail applications, word processing application, spreadsheetapplications, slide presentation applications, and the like). Thisscoring factor insures personalized suggestions may be generated for theuser where user behavior with respect to the first scoring factor is notavailable. Such would be the case when a given user first utilizes thesuggestion application, where no user suggestion behavior has beenestablished. This scoring factor may also serve as a tie-breaker ifmultiple persons are associated with a same suggestion usage history forthe querying user. This scoring factor may be formed by collectinginformation from other applications the querying user utilizes duringhis/her daily work life. According to one embodiment, multiple scoringfactors may be combined into a single score.

According to one embodiment, when the application 120 needs to rank aset of people from a repository of people, for example, an enterprisedirectory of people, that match a particular user query, the applicationmay extract a relationship edge (relationship between the querying userand the being-scored person) from the graph 114 (described above withreference to FIG. 1) and the usage weight described above for the firstscoring factor for generating the combined score used in ranking a givenperson against other persons as suggestion candidates. The application120 ranks people by the final score which is stored in the applicationand that is composed of the sum of the usage weight and the relationshipedge weight (normalized into a decimal value).

According to this embodiment, the score for the relationship edge isnormalized to a decimal value so that it contributes less than theuser's suggestion usage score. Thus, a person is ranked by how muchsuggestion usage that person has recently received by the querying user,as well as, weighting of the relationship information between thequerying user and the person (e.g., manager versus peer). In case of atie with other people based on usage, the relationship edge weightingmay be used for tie breaking. In the absence of usage information,relationship edge weighting may be used for people ranking.

After scoring of individual people as candidates for suggesting to thequerying user as people query suggestions, the candidates may be rankedby the suggestion application 120 in a ranking operation 220 based onthe scoring applied to each person. Scored and ranked people informationmay be stored to cache at (local or backend storage) for subsequentrecall. After ranking, a top X number (e.g., top 10) of ranked peoplemay be returned at operation 225 for providing people suggestions to thequerying user. According to embodiments, scoring information for thosepeople not ranking high enough for inclusion in an initial return ofpeople query suggestions is not discarded. Such scoring information maybe updated from time-to-time based on additional user interactions withthose people, and in response to a future query, such people may bepresented to the user in a query suggestion.

According to one embodiment, when the user opens the suggestionapplication 120, the top X (e.g., top 100) people with highestrelationship edges (weights) are retrieved from the server (backend) 125into the local cache at the application 120, and their respectiverelationship edge weights are utilized that contribute as a factor intheir final score for sorting. In this case, certain people are alreadyin the application cache, and the application 120 may simply update thenormalized component of the social weight. According to anotherembodiment, the suggestion application 120 may update that top X peoplelist and the associated weights on a periodic basis (e.g., every 23hours). This ensures the suggestion application 120 has people (relevantand/or interesting to the querying user) in the cache that may beaccessed instantly (upon application opening) without having to querythe backend 125. That is, this process gives the application 120 astarting point of people suggestions before additional processing isaccomplished or required.

For the second type of querying suggestion (i.e., actor-actionsuggestion), at operation 230, the suggestion application 120 firstreturns textual suggestions associated with the querying user as thequerying user is more likely to care his/her own content items thancontent items associated with other people. In this case, the actorportion of the actor/action suggestion is the querying user, and theaction portion of the suggestion involves a content item actionassociated with a particular content item. For example, such a textualsuggestion may include “edited by me” wherein selecting such a textualsuggestion may cause a navigation to a document edited by the queryinguser. According to one embodiment, in response to a selection of anactor-action suggestion, the suggestion application 120 may present ornavigate the user to a set of results that may contain none, one or manycontent items. From there, the user may interact with individual itemsas desired.

According to one embodiment, at operation 235, a most-used suggestionfrom the querying user's last X suggestion application uses may beprovided. According to one embodiment, the suggestion application 120not only presents the user with one suggestion he/she has used before,but the application may return as many matches as can be fit into thesuggestion slots ordered by occurrence (and tie broken by selecting mostrecent matching uses if necessary). The same is true for suggestionswhere the user is the actor (operation 230). All matches are returned,ordered by action priority and any remaining space for suggestions maybe used in the next suggestion generation operation (operation 235).

As described above, in the case of ties between two textual suggestions,more recent textual suggestions may be provided. That is, according toone embodiment, the application 120 may sort any matches from cache byoccurrence (how many times a particular suggestion was used), and incase of ties, the application 120 may look at a last used date for agiven suggestion as a tie breaker. When that cache gets full and a newuse has to be inserted, the application 120 may replace the leastrecently used entry.

In addition to textual suggestions associated with the querying user, atoperation 240, people returned as part of the people query suggestions,described above at operation 225, may be used for finding actionsassociated with those people, wherein the returned people are the actorsportion of the actor-action textual suggestion, and wherein the actionportion of the suggestion involves a content item action associated witha particular content item. That is, if after the above processing, slotsfor suggestions in the suggestion user interface are still available,the application 120 may use the people suggestions being shown to theuser for generating more textual suggestions. In this case, theapplication 120 may have a prioritized list of actions (e.g., presentedto, edited by, liked by, etc.) and a prioritized list of people (fromthe people suggestions generated above). For each person, ordered by howthey are ranked as suggestions, the application 120 may create textualactor-action suggestions. For example, if the first person beingsuggested is John Doe and the second person being suggested is JaneSmith, the suggestion application 120 may first create all textualsuggestions with John Doe (e.g., presented to John Doe, edited by JohnDoe, etc.), followed by creating textual suggestions for Jane Smith. Theapplication 120 may then move to a third person suggestion, generatetextual suggestions with that person as the actor before moving to anext person, and so on.

According to one embodiment, a number of people suggestions andactor-action text suggestions may be generated on a periodic basis andmay be stored for recall and presentation, as illustrated and describedbelow with respect to FIGS. 3A-3G. Alternatively, the process ofgenerating people suggestions and actor-action text suggestions,described above, may be performed dynamically as a user enters a querystring for obtaining one or more suggestions. The method 200 ends atoperation 295.

After the two types of query suggestions described above are generatedfor a querying user, the querying user is then ready to receive thegenerated query suggestions in response to his/her query input, and theuser may then select a desired suggestion for navigating to people oractions/activities associated with suggested people, including thequerying user himself/herself. In FIGS. 3A-3G, example user interfaceswith which a querying user may interact with the suggestion application120 described herein are illustrated. As should be appreciated, the userinterfaces and functionality components and content items displayedtherein, as illustrated in FIGS. 3A-3G are for purposes of illustrationand example only and are not limiting of the vast numbers of userinterface components and layouts that may be utilized in accordance withembodiments of the present invention.

FIG. 3A is an illustration of an example information page comprising agrid of aggregated content items from which a query may be initiated forreceiving one or more personalized query suggestions. An exampleinformation page (also referred to as a landing page) 302A isillustrated that may be displayed on any suitable computing device 118described above. The landing page 302A may comprise a plurality ofcontent items 103 A-F (collectively 103) displayed in a grid. Thecontent items 103 may be organized and ordered according to a relevanceranking. According to an embodiment, the content items 103 may bedisplayed as selectable objects that may comprise one or more of avisual representation of the content item 103 (e.g., a thumbnail imageor other salient image that is extracted from the content item 103), thetitle of the content item 103, activity insights (e.g., number of views,a number of likes, a number of followers, a number of comments, etc.), asummary or brief description of the content item, 103, etc. Otherinformation may also be provided, such as an individual 102 the user 122shares a relationship edge 112A with who has acted on the content item103, the action taken, and how recently the action took place. Forexample, as illustrated in FIG. 3A, the first content item 103A showsthat an individual 102 Liz Andrews modified the content item 103A “aboutan hour ago.” According to embodiments, a user 122 may navigate to apredefined or to a user-defined query via selection of a navigationcontrol 304. As illustrated in FIG. 3A, a title or header of a page 302Amay be a selectable navigation control 304. When selected, the user 122may select from a predefined query or may enter a search query, forexample, a search query for one or more query suggestions describedherein.

Referring now to FIG. 3B, the example information page 302A isillustrated showing a query field in which a query may be initiated forreceiving one or more personalized query suggestions. In the query field308, a user may initiate a query for one or more query suggestions bybeginning character entry of a natural language text string. In theexample shown, the user has entered the textual character “P” in thefield 308. According to embodiments, the example character “P” may bepart of an eventual word or sentence or the character may be the onlycharacter the user intends to enter. As should be appreciated, input ofthe initial character “P” by the user may be the beginning of a queryfor a person named Paul Jones, Peter Brown, Pamela Green, Pedro Manerez,Henry Pope, James Peterson, and the like. That is, any person's name(first name or last name) beginning with the character “P” may be aperson's name to which the user's query is directed. Likewise, theinitial input of the character “P” may be the beginning of a naturallanguage text string being entered by the user for searching on suchactions as “presented to me”, “provided by Joe”, “processed by Sara”,“edited by Paul,” “viewed by Henry Pope,” and the like. That is, anynatural language text string that includes the example character “P” maybe an intended query being entered by the user. As described herein, assoon as the user enters the first character, for example, the character“P”, the suggestion application 120 may search stored suggestions ordynamically generate suggestions, as described above with reference toFIGS. 1 and 2, for provision to the querying user in the user interfacepage 302A.

Referring now to FIG. 3C, in response to the initial input of theexample character “P”, the suggestion application 120 automaticallyretrieves a top X (e.g., top 4) people suggestions having namesbeginning with the entered text character and automatically retrieves atop X ranked actor-action text suggestions being associated with peoplewhose names begin with the entered character or associated with actionsassociated with the entered character. According to embodiments of thepresent invention, graphical representations of the retrieved peoplesuggestions and actor-action suggestions may be presented in an array ofsuggestions in the landing page 302 a. According to one embodiment, thepeople suggestions may be placed in columns and rows in one portion ofthe landing page, and the actor-action text suggestions may be presentedadjacent to the people suggestions in a section portion of the landingpage 302 a, as illustrated in FIG. 3C.

As illustrated in FIG. 3C, four columns and two rows of peoplesuggestions and actor-action suggestions are presented, and variousdocuments or content items previously presented in the user's landingpage 302 a are persisted in the display of the landing page beneath adisplay of the newly displayed people and/or actor-action suggestions.As should be appreciated, the layout illustrated in FIG. 3C is forpurposes of example only, and is not limiting of other layouts that maybe utilized as desired. For example, a greater number of peoplesuggestions and/or actor-action suggestions may be displayed than areillustrated in FIG. 3C, or according to a different display option, thedocuments 103 a, 103 b, 103 c and 103 d may not be persisted in thedisplay underneath or otherwise relative to the display of the varioussuggestions. For another example, while, according to one embodiment,both of the two types of suggestions are provided in response to a givenquery, either type may be provided without providing the other type inresponse to a given query. In addition, the number of people and/oractor-action suggestions that may be presented to a user may beoptimized based on the screen size in use by the user. For example, ifthe user is using a small form device such as a tablet computer or smarttelephone, a smaller number suggestions may be provided and displayed,whereas, if the user is using a large form computer display, a greaternumber of suggestions may be provided and displayed in response to auser query.

Referring still to FIG. 3C, a first people suggestion 315 is illustratedfor an example person “Paula Wallace”. As illustrated, an image of theperson including a photograph, avatar, or other identifying image may bepresented for providing the querying user a quick reference point as tothe identity of the returned people suggestion. In addition, textualinformation such as the person's name, title, or other information maybe provided near the provided image. According to embodiments, the imageand associated text for the people suggestion is a selectable icon whichwhen selected may cause an immediate navigation to information about theperson associated with the suggestion 315, as described below. Otherpeople suggestions 320, 325, 330 are similarly illustrated in FIG. 3C.

Adjacent to the four example people suggestions 315-330 is an array ofactor-action suggestions 335, 340, 345, 350. As with the peoplesuggestions, the actor-action suggestions are those suggestionsresponsive to the query input received from the querying user. Forexample, as the querying user initially entered the character “P”, thenactor-action suggestions returned include either actors (people) whosenames begin with entered textual character, or actions associated withthe actor-action suggestions that begin with the entered character. Forexample, in response to an entered character of “P”, an actor-actionsuggestion of “presented to me” 335 is provided where the actor is thequerying user (i.e., me) and the action associated with the actor is“presented to”. This example actor-action suggestion may be associatedwith a document or other content item presented to the querying user.Selection of the suggestion 335 may cause immediate navigation to thecontent item, for example, a document, that has been presented to theuser at some point in time and that has been ranked sufficiently high tobe presented as a first actor-action suggestion in response to theuser's initiated query.

Other actor-action suggestions include the “popular near me” suggestion340, the “edited by Paula Wallace” suggestion 345, and the “popular nearPeter Smith” suggestion 350. As described above with respect to FIG. 2,the actor-action suggestions are generated by first matching any “me”(i.e., the querying user) suggestions ordered by action priority,followed by matching any suggestion the querying user has used before(i.e., among his/her X uses) and sorting by occurrence (and tie brokenby selecting most recent matching uses if necessary), followed by usingpeople suggestions to generate actor-action suggestions if required.

According to embodiments, each of the actor-action suggestions 335-350presented to the user, may include an image, photograph, clipart, andthe like along with a text string that identifies both the actor and theaction associated with the suggestion. For example, if the actionassociated with a given actor-action suggestion deals with a spreadsheetchart, then a spreadsheet chart icon may be provided as a selectableicon for the suggestion. As should be appreciated, any variety of iconsmay be utilized for conveying information to the querying user about thenature of the actor-action suggestion.

Referring still to FIGS. 3B and 3C, if the querying user enters a secondor more text characters in the entered query, the returned people andactor-action suggestions will be automatically filtered based on theadditional query input. For example, as illustrated and described above,in response to entering the character “P”, people suggestions andactor-action suggestions beginning with the character “P” areautomatically returned. As should be appreciated, hundreds or thousandsof people or actor-actions suggestions beginning with the examplecharacter “P” may be returned by the suggestion application 102, and atop X ranked items of each type of suggestion may be provided in theuser interface 302A.

However, if an additional text character is entered, for example, thecharacter “E” where a resulting query input of “PE” is now entered, theresulting query input will be used for automatically filtering thesuggestion results such that people suggestions and actor-actionsuggestions having the characters “PE” will be returned. Thus, forexample, the people “Paula Wallace” 315, “Pamela Andrews” 325, and “PaulBrown” 330 will be automatically filtered out of the people suggestions,and people having names beginning with the characters “PE” will beprovided. Likewise, the actor-action suggestions will be filtered toinclude actor-action suggestions associated with the querying user inassociation with actions having the characters “PE” in the associatedactions, as well as, new actor-action suggestions where the actor havenames having the characters “PE” filtered accordingly. As should beappreciated this filtering process may continue as long as the userenters additional characters into the search query.

Referring now to FIG. 3D, selection of a given people suggestion 315 isillustrated. The selection illustrated in FIG. 3D is in the form of auser tap gesture on a touch-enabled computing device, but as should beappreciated, any suitable user interaction, for example, keyboardinteraction, mouse interaction, electronic pen/ink interaction, voiceinteraction, gesture interaction, eye tracking interaction, and the likemay be utilized for selecting a given suggestion (and for entering querystrings, as described herein).

Referring now to FIG. 3E, in response to a selection on the examplepeople suggestion 315, a navigation to information associated with theperson for whom the people suggestion 315 was provided may be presentedto the querying user. As illustrated in FIG. 3E, an example oforganizational chart information for the person associated with theselected people suggestion is illustrated. For example, organizationalinformation 355 for the selected person is provided. Manager information357 is illustrated, and an array of other people 360 with which both theselected person and her manager work is provided. An array of people 365working with the selected person 355 is presented. An array of peers 370of the selected person is presented, and an array of direct reports 375for the selected person is presented. As should be appreciated, theinformation illustrated in FIG. 3E is for purposes of example andillustration only. Any number of other information items associated withthe selected person, for example, employment history, statisticalinformation, contact information, documents associated with, and thelike may presented in a user interface, as is illustrated in FIG. 3E, inresponse to a selection of the associated people suggestion 315.

Thus, in accordance with embodiments of the present invention, if a userbegan an initial query by typing the character “P” because the user wasinterested in retrieving contact information for her colleague “PamelaWallace,” then the people suggestion 315 allows the user to quicklyselect the associated people suggestion for navigating to informationfrom which the user may retrieve the desired information. Because thesuggestions provided by the suggestion application 120 are personalizedto the querying user, as described above, then the suggestions and theinformation that may be navigated to upon selection of the suggestionsis more likely to be relevant to or of particular interest to thequerying user.

Referring to FIG. 3F, a selection of an actor-action suggestion 335 isillustrated. In response to selection of the actor-action suggestion335, navigation to a document or other content item that is the subjectof the selected suggestion is automatically provided, as illustrated inFIG. 3G. Referring to FIG. 3G, an example document (e.g., a spreadsheetapplication document 380 having one or more data items 385) is presentedto the user. Thus, referring back to FIG. 3F, when the user waspresented with the example actor-action suggestion of “presented to me,”selection of the suggestion allows for an immediate navigation to thedocument or other content item that was presented to the user. Asdescribed above with respect to people suggestions, because theactor-action suggestions are personalized for the querying user, thenthere is a greater likelihood that information associated with theprovided suggestions is more relevant or interesting to the queryinguser.

According to embodiments, each time a user selects a people suggestionor actor-action suggestion, scoring associated with the people oractions associated with the selected action is increased. For example,if a user selects a people suggestion 315 associated with a person“Paula Wallace,” then the person “Paula Wallace” will be scored higherin a subsequent scoring and ranking process, as described above fordetermining whether a people suggestion for the selected person shouldrank higher than other people suggestions. Likewise, a selection of anactor-action suggestion may cause a person associated with theactor-action suggestion or a document or other content item associatedwith the actor-action suggestion to be scored higher and subsequentlyranked higher in future instances of provided suggestions.

While the invention has been described in the general context of programmodules that execute in conjunction with an application program thatruns on an operating system on a computer, those skilled in the art willrecognize that the invention may also be implemented in combination withother program modules. Generally, program modules include routines,programs, components, data structures, and other types of structuresthat perform particular tasks or implement particular abstract datatypes.

The embodiments and functionalities described herein may operate via amultitude of computing systems including, without limitation, desktopcomputer systems, wired and wireless computing systems, mobile computingsystems (e.g., mobile telephones, netbooks, tablet or slate typecomputers, notebook computers, and laptop computers), hand-held devices,multiprocessor systems, microprocessor-based or programmable consumerelectronics, minicomputers, and mainframe computers.

In addition, the embodiments and functionalities described herein mayoperate over distributed systems (e.g., cloud-based computing systems),where application functionality, memory, data storage and retrieval andvarious processing functions may be operated remotely from each otherover a distributed computing network, such as the Internet or anintranet. User interfaces and information of various types may bedisplayed via on-board computing device displays or via remote displayunits associated with one or more computing devices. For example userinterfaces and information of various types may be displayed andinteracted with on a wall surface onto which user interfaces andinformation of various types are projected. Interaction with themultitude of computing systems with which embodiments of the inventionmay be practiced include, keystroke entry, touch screen entry, voice orother audio entry, gesture entry where an associated computing device isequipped with detection (e.g., camera) functionality for capturing andinterpreting user gestures for controlling the functionality of thecomputing device, and the like.

FIGS. 4-6 and the associated descriptions provide a discussion of avariety of operating environments in which embodiments of the inventionmay be practiced. However, the devices and systems illustrated anddiscussed with respect to FIGS. 4-6 are for purposes of example andillustration and are not limiting of a vast number of computing deviceconfigurations that may be utilized for practicing embodiments of theinvention, described herein.

FIG. 4 is a block diagram illustrating physical components (i.e.,hardware) of a computing device 400 with which embodiments of theinvention may be practiced. The computing device components describedbelow may be suitable for the client device 118 described above. In abasic configuration, the computing device 400 may include at least oneprocessing unit 402 and a system memory 404. Depending on theconfiguration and type of computing device, the system memory 404 maycomprise, but is not limited to, volatile storage (e.g., random accessmemory), non-volatile storage (e.g., read-only memory), flash memory, orany combination of such memories. The system memory 404 may include anoperating system 405 and one or more program modules 406 suitable forrunning software applications 450 such as the aggregator 108, analyticsengine 115, or client application 120. The operating system 405, forexample, may be suitable for controlling the operation of the computingdevice 400. Furthermore, embodiments of the invention may be practicedin conjunction with a graphics library, other operating systems, or anyother application program and is not limited to any particularapplication or system. This basic configuration is illustrated in FIG. 4by those components within a dashed line 408. The computing device 400may have additional features or functionality. For example, thecomputing device 400 may also include additional data storage devices(removable and/or non-removable) such as, for example, magnetic disks,optical disks, or tape. Such additional storage is illustrated in FIG. 4by a removable storage device 409 and a non-removable storage device410.

As stated above, a number of program modules and data files may bestored in the system memory 404. While executing on the processing unit402, the program modules 406 may perform processes including, but notlimited to, one or more of the stages of the method 200 illustrated inFIG. 2. Other program modules that may be used in accordance withembodiments of the present invention and may include applications suchas electronic mail and contacts applications, word processingapplications, spreadsheet applications, database applications, slidepresentation applications, drawing or computer-aided applicationprograms, etc.

Furthermore, embodiments of the invention may be practiced in anelectrical circuit comprising discrete electronic elements, packaged orintegrated electronic chips containing logic gates, a circuit utilizinga microprocessor, or on a single chip containing electronic elements ormicroprocessors. For example, embodiments of the invention may bepracticed via a system-on-a-chip (SOC) where each or many of thecomponents illustrated in FIG. 4 may be integrated onto a singleintegrated circuit. Such an SOC device may include one or moreprocessing units, graphics units, communications units, systemvirtualization units and various application functionality all of whichare integrated (or “burned”) onto the chip substrate as a singleintegrated circuit. When operating via an SOC, the functionality,described herein, with respect to providing a personalized view ofinsights into social activity surrounding a content item 103 may beoperated via application-specific logic integrated with other componentsof the computing device 400 on the single integrated circuit (chip).Embodiments of the invention may also be practiced using othertechnologies capable of performing logical operations such as, forexample, AND, OR, and NOT, including but not limited to mechanical,optical, fluidic, and quantum technologies. In addition, embodiments ofthe invention may be practiced within a general purpose computer or inany other circuits or systems.

The computing device 400 may also have one or more input device(s) 412such as a keyboard, a mouse, a pen, a sound input device, a touch inputdevice, etc. The output device(s) 414 such as a display, speakers, aprinter, etc. may also be included. The aforementioned devices areexamples and others may be used. The computing device 400 may includeone or more communication connections 416 allowing communications withother computing devices 418. Examples of suitable communicationconnections 416 include, but are not limited to, RF transmitter,receiver, and/or transceiver circuitry; universal serial bus (USB),parallel, and/or serial ports.

The term computer readable media as used herein may include computerstorage media. Computer storage media may include volatile andnonvolatile, removable and non-removable media implemented in any methodor technology for storage of information, such as computer readableinstructions, data structures, or program modules. The system memory404, the removable storage device 409, and the non-removable storagedevice 410 are all computer storage media examples (i.e., memorystorage.) Computer storage media may include RAM, ROM, electricallyerasable read-only memory (EEPROM), flash memory or other memorytechnology, CD-ROM, digital versatile disks (DVD) or other opticalstorage, magnetic cassettes, magnetic tape, magnetic disk storage orother magnetic storage devices, or any other article of manufacturewhich can be used to store information and which can be accessed by thecomputing device 400. Any such computer storage media may be part of thecomputing device 400. Computer storage media does not include a carrierwave or other propagated or modulated data signal.

Communication media may be embodied by computer readable instructions,data structures, program modules, or other data in a modulated datasignal, such as a carrier wave or other transport mechanism, andincludes any information delivery media. The term “modulated datasignal” may describe a signal that has one or more characteristics setor changed in such a manner as to encode information in the signal. Byway of example, and not limitation, communication media may includewired media such as a wired network or direct-wired connection, andwireless media such as acoustic, radio frequency (RF), infrared, andother wireless media.

FIGS. 5A and 5B illustrate a mobile computing device 500, for example, amobile telephone, a smart phone, a tablet personal computer, a laptopcomputer, and the like, with which embodiments of the invention may bepracticed. With reference to FIG. 5A, one embodiment of a mobilecomputing device 500 for implementing the embodiments is illustrated. Ina basic configuration, the mobile computing device 500 is a handheldcomputer having both input elements and output elements. The mobilecomputing device 500 typically includes a display 505 and one or moreinput buttons 510 that allow the user to enter information into themobile computing device 500. The display 505 of the mobile computingdevice 500 may also function as an input device (e.g., a touch screendisplay). If included, an optional side input element 515 allows furtheruser input. The side input element 515 may be a rotary switch, a button,or any other type of manual input element. In alternative embodiments,mobile computing device 500 may incorporate more or less input elements.For example, the display 505 may not be a touch screen in someembodiments. In yet another alternative embodiment, the mobile computingdevice 500 is a portable phone system, such as a cellular phone. Themobile computing device 500 may also include an optional keypad 535.Optional keypad 535 may be a physical keypad or a “soft” keypadgenerated on the touch screen display. In various embodiments, theoutput elements include the display 505 for showing a graphical userinterface (GUI), a visual indicator 520 (e.g., a light emitting diode),and/or an audio transducer 525 (e.g., a speaker). In some embodiments,the mobile computing device 500 incorporates a vibration transducer forproviding the user with tactile feedback. In yet another embodiment, themobile computing device 500 incorporates input and/or output ports, suchas an audio input (e.g., a microphone jack), an audio output (e.g., aheadphone jack), and a video output (e.g., a HDMI port) for sendingsignals to or receiving signals from an external device.

FIG. 5B is a block diagram illustrating the architecture of oneembodiment of a mobile computing device. That is, the mobile computingdevice 500 can incorporate a system (i.e., an architecture) 502 toimplement some embodiments. In one embodiment, the system 502 isimplemented as a “smart phone” capable of running one or moreapplications (e.g., browser, e-mail, calendaring, contact managers,messaging clients, games, and media clients/players). In someembodiments, the system 502 is integrated as a computing device, such asan integrated personal digital assistant (PDA) and wireless phone.

One or more application programs 550 may be loaded into the memory 562and run on or in association with the operating system 564. Examples ofthe application programs include phone dialer programs, e-mail programs,personal information management (PIM) programs, word processingprograms, spreadsheet programs, Internet browser programs, messagingprograms, and so forth. The system 502 also includes a non-volatilestorage area 568 within the memory 562. The non-volatile storage area568 may be used to store persistent information that should not be lostif the system 502 is powered down. The application programs 550 may useand store information in the non-volatile storage area 568, such ase-mail or other messages used by an e-mail application, and the like. Asynchronization application (not shown) also resides on the system 502and is programmed to interact with a corresponding synchronizationapplication resident on a host computer to keep the information storedin the non-volatile storage area 568 synchronized with correspondinginformation stored at the host computer. As should be appreciated, otherapplications may be loaded into the memory 562 and run on the mobilecomputing device 500.

The system 502 has a power supply 570, which may be implemented as oneor more batteries. The power supply 570 might further include anexternal power source, such as an AC adapter or a powered docking cradlethat supplements or recharges the batteries.

The system 502 may also include a radio 572 that performs the functionof transmitting and receiving radio frequency communications. The radio572 facilitates wireless connectivity between the system 502 and the“outside world,” via a communications carrier or service provider.Transmissions to and from the radio 572 are conducted under control ofthe operating system 564. In other words, communications received by theradio 572 may be disseminated to the application programs 150 via theoperating system 564, and vice versa.

The visual indicator 520 may be used to provide visual notificationsand/or an audio interface 574 may be used for producing audiblenotifications via the audio transducer 525. In the illustratedembodiment, the visual indicator 520 is a light emitting diode (LED) andthe audio transducer 525 is a speaker. These devices may be directlycoupled to the power supply 570 so that when activated, they remain onfor a duration dictated by the notification mechanism even though theprocessor 560 and other components might shut down for conservingbattery power. The LED may be programmed to remain on indefinitely untilthe user takes action to indicate the powered-on status of the device.The audio interface 574 is used to provide audible signals to andreceive audible signals from the user. For example, in addition to beingcoupled to the audio transducer 525, the audio interface 574 may also becoupled to a microphone to receive audible input, such as to facilitatea telephone conversation. In accordance with embodiments of the presentinvention, the microphone may also serve as an audio sensor tofacilitate control of notifications, as will be described below. Thesystem 502 may further include a video interface 576 that enables anoperation of an on-board camera 530 to record still images, videostream, and the like.

A mobile computing device 500 implementing the system 502 may haveadditional features or functionality. For example, the mobile computingdevice 500 may also include additional data storage devices (removableand/or non-removable) such as, magnetic disks, optical disks, or tape.Such additional storage is illustrated in FIG. 5B by the non-volatilestorage area 568.

Data/information generated or captured by the mobile computing device500 and stored via the system 502 may be stored locally on the mobilecomputing device 500, as described above, or the data may be stored onany number of storage media that may be accessed by the device via theradio 572 or via a wired connection between the mobile computing device500 and a separate computing device associated with the mobile computingdevice 500, for example, a server computer in a distributed computingnetwork, such as the Internet. As should be appreciated suchdata/information may be accessed via the mobile computing device 500 viathe radio 572 or via a distributed computing network. Similarly, suchdata/information may be readily transferred between computing devicesfor storage and use according to well-known data/information transferand storage means, including electronic mail and collaborativedata/information sharing systems.

FIG. 6 illustrates one embodiment of the architecture of a system forproviding an aggregated view of top ranking content items 103 based onrelevance to a user 122, as described above. Content developed,interacted with, or edited in association with the application 120 maybe stored in different communication channels or other storage types.For example, various documents may be stored using a directory service622, a web portal 624, a mailbox service 626, an instant messaging store628, or a social networking site 630. The application 120 may use any ofthese types of systems or the like for providing query suggestions, asdescribed herein. A server 615 may provide the application 120 toclients 118. As one example, the server 615 may be a web serverproviding the functionality of the application 120 over the web. Theserver 615 may provide the functionality of the application 120 over theweb to clients 118 through a network 610. By way of example, the clientcomputing device 118 may be implemented and embodied in a personalcomputer 605A, a tablet computing device 605B and/or a mobile computingdevice 605C (e.g., a smart phone), or other computing device. Any ofthese embodiments of the client computing device may obtain content fromthe store 616.

Embodiments of the present invention, for example, are described abovewith reference to block diagrams and/or operational illustrations ofmethods, systems, and computer program products according to embodimentsof the invention. The functions/acts noted in the blocks may occur outof the order as shown in any flowchart. For example, two blocks shown insuccession may in fact be executed substantially concurrently or theblocks may sometimes be executed in the reverse order, depending uponthe functionality/acts involved.

The description and illustration of one or more embodiments provided inthis application are not intended to limit or restrict the scope of theinvention as claimed in any way. The embodiments, examples, and detailsprovided in this application are considered sufficient to conveypossession and enable others to make and use the best mode of claimedinvention. The claimed invention should not be construed as beinglimited to any embodiment, example, or detail provided in thisapplication. Regardless of whether shown and described in combination orseparately, the various features (both structural and methodological)are intended to be selectively included or omitted to produce anembodiment with a particular set of features. Having been provided withthe description and illustration of the present application, one skilledin the art may envision variations, modifications, and alternateembodiments falling within the spirit of the broader aspects of thegeneral inventive concept embodied in this application that do notdepart from the broader scope of the claimed invention.

What is claimed is:
 1. A method of personalizing query suggestions, comprising: receiving a query via computer-enabled query application; determining a first score for each of one or more people, wherein the first score is based on a frequency with which a querying user has selected each of the one or more people from previous query suggestions produced by the query application; determining a second score for each of the one or more people based on interactions between the querying user and the one or more people in one or more applications other than the query application; combining the first score and the second score to form a combined score, wherein the second score contributes less than the first score to the combined score; ranking each of the one or more people based on the combined score; providing, based on the ranking, a people query suggestion in a query suggestion user interface; and providing an actor-action query suggestion in the query suggestion user interface for receiving information about a content item action associated with a person corresponding to the content item action.
 2. The method of claim 1, wherein receiving a query via a computer-enabled query application includes receiving a textual input; wherein providing the people query suggestion includes providing a people query suggestion matching the textual input; and wherein providing the actor-action query suggestion includes providing an actor-action query suggestion matching the textual input.
 3. The method of claim 2, in response to receiving an additional textual input, updating the provided people query suggestion to provide an updated people query suggestion, and updating the provided actor-action query suggestion to provide an updated actor-action query suggestion based on the additional textual query input.
 4. The method of claim 1, further comprising receiving a selection of the people query suggestion and automatically providing information about the corresponding person in a computer-enabled user interface.
 5. The method of claim 4, further comprising receiving a selection of the actor-action and automatically navigating to a content item associated with the content item action.
 6. The method of claim 4, in response to receiving the selection of the people query suggestion, increasing a score associated with a person for which the people query suggestion is provided, the increased score being used for subsequent ranking applied to the person for which the people query suggestion is provided.
 7. The method of claim 6, in response to receiving the selection of the actor-action query suggestion, ranking the actor-action query higher for subsequent provision as an actor-action query suggestion.
 8. The method of claim 1, further comprising providing an additional actor-action query suggestion for content item actions associated with the querying user.
 9. The method of claim 1, further comprising providing an additional actor-action query suggestion based on a most used actor-action query suggestion from the querying user's last X uses, where X is a number greater than or equal to one.
 10. The method of claim 9, wherein providing the additional actor-action query suggestion comprises providing the additional actor-action query suggestion based on the most used actor-action query suggestion and on content item actions corresponding to a person for which the people query suggestion is provided.
 11. The method of claim 1, further comprising prior to combining the first and the second scores, normalizing the second score to a decimal value such that the second score contributes less to the combined final score.
 12. A computer readable storage device on which is stored computer executable instructions which, when executed by a computer, perform a method of personalizing query suggestions, comprising: receiving a query via computer-enabled query application; determining a first score for each of one or more people, wherein the first score is based on a frequency with which a querying user has selected each of the one or more people from previous query suggestions produced by the query application; determining a second score for each of the one or more people based on interactions between the querying user and the one or more people in one or more additional applications; combining the first score and the second score to form a combined score, wherein the second score is normalized to a decimal value when combined with the first score; ranking each of the one or more people based on the combined score; and providing, based on the ranking, a people query suggestion, wherein providing the people query suggestion includes providing the people query suggestion for a top ranked of the one or more people.
 13. The computer readable storage device of claim 12, further comprising providing an actor-action query suggestion for querying information about a content item action associated with a person corresponding to the content item action.
 14. The computer readable storage device of claim 13, wherein receiving a query via a computer-enabled query application includes receiving a textual input; wherein providing the people query suggestion includes providing a people query suggestion matching the textual input; and wherein providing the actor-action query suggestion includes providing an actor-action query suggestion matching the textual input.
 15. The computer readable storage device of claim 13, in response to receiving an additional textual input, updating the provided people query suggestion to provide an updated people query suggestion, and updating the provided actor-action query suggestion to provide an updated actor-action query suggestion based on the additional textual query input.
 16. The computer readable storage device of claim 13, further comprising at least one of: receiving a selection of the people query suggestion and automatically providing information about the corresponding person in a computer-enabled user interface; or receiving a selection of the actor-action and automatically navigating to a content item associated with the content item action.
 17. A computer system comprising a computer processor in communication with computer-readable storage device, the computer-readable storage device storing instructions that, when executed, perform a method, the method comprising: receiving a query via computer-enabled query application; determining a first score for each of one or more people, wherein the first score is based on a frequency with which a querying user has selected each of the one or more people from previous query suggestions produced by the query application; determining a second score for each of the one or more people based on interactions between the querying user and the one or more people in one or more additional applications; combining the first score and the second score to form a combined score, wherein the second score contributes less than the first score to the combined score; ranking each of the one or more people based on the combined score; and providing, based on the ranking, a people query suggestion, wherein providing the people query suggestion includes providing the people query suggestion for a top ranked of the one or more people.
 18. The computer system of claim 17, wherein the method further comprises prior to combining the first and the second scores, normalizing the second score to a decimal value such that the second score contributes less to the combined score.
 19. The computer system of claim 17, wherein the method further comprises providing an actor-action query suggestion for querying information about a content item action associated with a person corresponding to the content item action.
 20. The computer system of claim 19, wherein the method further comprises at least one of: receiving a selection of the people query suggestion and automatically providing information about the corresponding person in a computer-enabled user interface; or receiving a selection of the actor-action and automatically navigating to a content item associated with the content item action. 